Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory344.9 KiB
Average record size in memory353.2 B

Variable types

DateTime1
Text2
Categorical2
Numeric6

Alerts

Destination Latitude is highly overall correlated with Destination LongitudeHigh correlation
Destination Longitude is highly overall correlated with Destination LatitudeHigh correlation
Source Latitude is highly overall correlated with Source LongitudeHigh correlation
Source Longitude is highly overall correlated with Source LatitudeHigh correlation
Timestamp has unique values Unique
Source IP has unique values Unique
Destination IP has unique values Unique

Reproduction

Analysis started2025-02-20 15:16:47.935368
Analysis finished2025-02-20 15:16:49.573536
Duration1.64 second
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Timestamp
Date

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2025-01-06 00:00:00
Maximum2025-02-16 15:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-20T10:16:49.605428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:49.651380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Source IP
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size60.9 KiB
2025-02-20T10:16:49.773798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.274
Min length9

Characters and Unicode

Total characters13274
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row96.32.89.114
2nd row201.61.222.13
3rd row51.31.102.145
4th row87.125.22.24
5th row106.16.28.144
ValueCountFrequency (%)
120.142.246.239 1
 
0.1%
153.17.251.196 1
 
0.1%
96.32.89.114 1
 
0.1%
201.61.222.13 1
 
0.1%
51.31.102.145 1
 
0.1%
87.125.22.24 1
 
0.1%
106.16.28.144 1
 
0.1%
205.149.201.212 1
 
0.1%
120.53.225.138 1
 
0.1%
10.145.68.198 1
 
0.1%
Other values (990) 990
99.0%
2025-02-20T10:16:49.930045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3000
22.6%
1 2557
19.3%
2 1669
12.6%
4 875
 
6.6%
3 867
 
6.5%
5 766
 
5.8%
9 756
 
5.7%
0 729
 
5.5%
8 707
 
5.3%
7 685
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3000
22.6%
1 2557
19.3%
2 1669
12.6%
4 875
 
6.6%
3 867
 
6.5%
5 766
 
5.8%
9 756
 
5.7%
0 729
 
5.5%
8 707
 
5.3%
7 685
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3000
22.6%
1 2557
19.3%
2 1669
12.6%
4 875
 
6.6%
3 867
 
6.5%
5 766
 
5.8%
9 756
 
5.7%
0 729
 
5.5%
8 707
 
5.3%
7 685
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3000
22.6%
1 2557
19.3%
2 1669
12.6%
4 875
 
6.6%
3 867
 
6.5%
5 766
 
5.8%
9 756
 
5.7%
0 729
 
5.5%
8 707
 
5.3%
7 685
 
5.2%

Destination IP
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size60.9 KiB
2025-02-20T10:16:50.029914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.252
Min length10

Characters and Unicode

Total characters13252
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row170.248.225.52
2nd row197.19.175.122
3rd row15.224.157.137
4th row31.233.27.146
5th row38.196.56.233
ValueCountFrequency (%)
170.209.108.122 1
 
0.1%
124.174.95.22 1
 
0.1%
170.248.225.52 1
 
0.1%
197.19.175.122 1
 
0.1%
15.224.157.137 1
 
0.1%
31.233.27.146 1
 
0.1%
38.196.56.233 1
 
0.1%
121.82.193.137 1
 
0.1%
85.207.133.168 1
 
0.1%
142.184.21.110 1
 
0.1%
Other values (990) 990
99.0%
2025-02-20T10:16:50.173631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3000
22.6%
1 2538
19.2%
2 1638
12.4%
3 890
 
6.7%
4 877
 
6.6%
5 788
 
5.9%
7 743
 
5.6%
8 702
 
5.3%
6 695
 
5.2%
9 694
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3000
22.6%
1 2538
19.2%
2 1638
12.4%
3 890
 
6.7%
4 877
 
6.6%
5 788
 
5.9%
7 743
 
5.6%
8 702
 
5.3%
6 695
 
5.2%
9 694
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3000
22.6%
1 2538
19.2%
2 1638
12.4%
3 890
 
6.7%
4 877
 
6.6%
5 788
 
5.9%
7 743
 
5.6%
8 702
 
5.3%
6 695
 
5.2%
9 694
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3000
22.6%
1 2538
19.2%
2 1638
12.4%
3 890
 
6.7%
4 877
 
6.6%
5 788
 
5.9%
7 743
 
5.6%
8 702
 
5.3%
6 695
 
5.2%
9 694
 
5.2%

Attack Type
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
DDoS
174 
SQL Injection
172 
Phishing
171 
Ransomware
166 
Insider Threat
160 

Length

Max length14
Median length10
Mean length9.299
Min length4

Characters and Unicode

Total characters9299
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDDoS
2nd rowRansomware
3rd rowDDoS
4th rowSQL Injection
5th rowRansomware

Common Values

ValueCountFrequency (%)
DDoS 174
17.4%
SQL Injection 172
17.2%
Phishing 171
17.1%
Ransomware 166
16.6%
Insider Threat 160
16.0%
Malware 157
15.7%

Length

2025-02-20T10:16:50.217283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T10:16:50.254643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ddos 174
13.1%
sql 172
12.9%
injection 172
12.9%
phishing 171
12.8%
ransomware 166
12.5%
insider 160
12.0%
threat 160
12.0%
malware 157
11.8%

Most occurring characters

ValueCountFrequency (%)
n 841
 
9.0%
e 815
 
8.8%
a 806
 
8.7%
i 674
 
7.2%
r 643
 
6.9%
o 512
 
5.5%
h 502
 
5.4%
s 497
 
5.3%
D 348
 
3.7%
S 346
 
3.7%
Other values (16) 3315
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 841
 
9.0%
e 815
 
8.8%
a 806
 
8.7%
i 674
 
7.2%
r 643
 
6.9%
o 512
 
5.5%
h 502
 
5.4%
s 497
 
5.3%
D 348
 
3.7%
S 346
 
3.7%
Other values (16) 3315
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 841
 
9.0%
e 815
 
8.8%
a 806
 
8.7%
i 674
 
7.2%
r 643
 
6.9%
o 512
 
5.5%
h 502
 
5.4%
s 497
 
5.3%
D 348
 
3.7%
S 346
 
3.7%
Other values (16) 3315
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 841
 
9.0%
e 815
 
8.8%
a 806
 
8.7%
i 674
 
7.2%
r 643
 
6.9%
o 512
 
5.5%
h 502
 
5.4%
s 497
 
5.3%
D 348
 
3.7%
S 346
 
3.7%
Other values (16) 3315
35.6%

Severity
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size53.1 KiB
Low
257 
High
256 
Critical
252 
Medium
235 

Length

Max length8
Median length6
Mean length5.221
Min length3

Characters and Unicode

Total characters5221
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCritical
2nd rowMedium
3rd rowCritical
4th rowMedium
5th rowCritical

Common Values

ValueCountFrequency (%)
Low 257
25.7%
High 256
25.6%
Critical 252
25.2%
Medium 235
23.5%

Length

2025-02-20T10:16:50.307225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T10:16:50.335072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low 257
25.7%
high 256
25.6%
critical 252
25.2%
medium 235
23.5%

Most occurring characters

ValueCountFrequency (%)
i 995
19.1%
L 257
 
4.9%
o 257
 
4.9%
w 257
 
4.9%
H 256
 
4.9%
g 256
 
4.9%
h 256
 
4.9%
C 252
 
4.8%
r 252
 
4.8%
t 252
 
4.8%
Other values (8) 1931
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 995
19.1%
L 257
 
4.9%
o 257
 
4.9%
w 257
 
4.9%
H 256
 
4.9%
g 256
 
4.9%
h 256
 
4.9%
C 252
 
4.8%
r 252
 
4.8%
t 252
 
4.8%
Other values (8) 1931
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 995
19.1%
L 257
 
4.9%
o 257
 
4.9%
w 257
 
4.9%
H 256
 
4.9%
g 256
 
4.9%
h 256
 
4.9%
C 252
 
4.8%
r 252
 
4.8%
t 252
 
4.8%
Other values (8) 1931
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 995
19.1%
L 257
 
4.9%
o 257
 
4.9%
w 257
 
4.9%
H 256
 
4.9%
g 256
 
4.9%
h 256
 
4.9%
C 252
 
4.8%
r 252
 
4.8%
t 252
 
4.8%
Other values (8) 1931
37.0%

Attempt Count
Real number (ℝ)

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.025
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-20T10:16:50.368861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum19
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.5107843
Coefficient of variation (CV)0.54970417
Kurtosis-1.1811198
Mean10.025
Median Absolute Deviation (MAD)5
Skewness0.0032340051
Sum10025
Variance30.368744
MonotonicityNot monotonic
2025-02-20T10:16:50.403509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
19 63
 
6.3%
16 62
 
6.2%
10 60
 
6.0%
8 58
 
5.8%
6 57
 
5.7%
9 56
 
5.6%
12 56
 
5.6%
2 56
 
5.6%
1 55
 
5.5%
11 55
 
5.5%
Other values (9) 422
42.2%
ValueCountFrequency (%)
1 55
5.5%
2 56
5.6%
3 53
5.3%
4 47
4.7%
5 43
4.3%
6 57
5.7%
7 46
4.6%
8 58
5.8%
9 56
5.6%
10 60
6.0%
ValueCountFrequency (%)
19 63
6.3%
18 45
4.5%
17 54
5.4%
16 62
6.2%
15 40
4.0%
14 48
4.8%
13 46
4.6%
12 56
5.6%
11 55
5.5%
10 60
6.0%

Data Volume (MB)
Real number (ℝ)

Distinct628
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.045
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-20T10:16:50.457084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile50
Q1268.75
median504.5
Q3728
95-th percentile954.1
Maximum999
Range997
Interquartile range (IQR)459.25

Descriptive statistics

Standard deviation282.69606
Coefficient of variation (CV)0.56647408
Kurtosis-1.0870347
Mean499.045
Median Absolute Deviation (MAD)230.5
Skewness0.011353094
Sum499045
Variance79917.062
MonotonicityNot monotonic
2025-02-20T10:16:50.588931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
611 6
 
0.6%
556 6
 
0.6%
328 6
 
0.6%
805 5
 
0.5%
537 5
 
0.5%
34 5
 
0.5%
28 4
 
0.4%
57 4
 
0.4%
594 4
 
0.4%
803 4
 
0.4%
Other values (618) 951
95.1%
ValueCountFrequency (%)
2 1
 
0.1%
5 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
13 1
 
0.1%
15 2
0.2%
16 3
0.3%
17 1
 
0.1%
18 1
 
0.1%
ValueCountFrequency (%)
999 1
 
0.1%
998 1
 
0.1%
997 4
0.4%
996 2
0.2%
994 2
0.2%
992 2
0.2%
990 2
0.2%
989 2
0.2%
988 2
0.2%
987 1
 
0.1%

Source Latitude
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.773874
Minimum-37.8136
Maximum55.9533
Zeros0
Zeros (%)0.0%
Negative205
Negative (%)20.5%
Memory size7.9 KiB
2025-02-20T10:16:50.620069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-37.8136
5-th percentile-37.8136
Q134.0522
median40.7128
Q349.2827
95-th percentile55.9533
Maximum55.9533
Range93.7669
Interquartile range (IQR)15.2305

Descriptive statistics

Standard deviation31.934806
Coefficient of variation (CV)1.1498146
Kurtosis0.0036043286
Mean27.773874
Median Absolute Deviation (MAD)6.6606
Skewness-1.3153389
Sum27773.874
Variance1019.8318
MonotonicityNot monotonic
2025-02-20T10:16:50.653953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-37.8136 81
 
8.1%
51.5074 77
 
7.7%
41.8781 75
 
7.5%
34.6937 74
 
7.4%
40.7128 71
 
7.1%
35.6895 69
 
6.9%
55.9533 68
 
6.8%
49.2827 67
 
6.7%
34.0522 66
 
6.6%
45.5017 65
 
6.5%
Other values (5) 287
28.7%
ValueCountFrequency (%)
-37.8136 81
8.1%
-33.8688 65
6.5%
-27.4698 59
5.9%
34.0522 66
6.6%
34.6937 74
7.4%
35.0116 48
4.8%
35.6895 69
6.9%
40.7128 71
7.1%
41.8781 75
7.5%
43.651 52
5.2%
ValueCountFrequency (%)
55.9533 68
6.8%
53.4808 63
6.3%
51.5074 77
7.7%
49.2827 67
6.7%
45.5017 65
6.5%
43.651 52
5.2%
41.8781 75
7.5%
40.7128 71
7.1%
35.6895 69
6.9%
35.0116 48
4.8%

Source Longitude
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.626183
Minimum-123.1207
Maximum153.0251
Zeros0
Zeros (%)0.0%
Negative604
Negative (%)60.4%
Memory size7.9 KiB
2025-02-20T10:16:50.698070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-123.1207
5-th percentile-123.1207
Q1-79.347
median-2.2426
Q3139.6917
95-th percentile153.0251
Maximum153.0251
Range276.1458
Interquartile range (IQR)219.0387

Descriptive statistics

Standard deviation106.62752
Coefficient of variation (CV)5.4329216
Kurtosis-1.6725061
Mean19.626183
Median Absolute Deviation (MAD)116.0011
Skewness0.11056936
Sum19626.183
Variance11369.427
MonotonicityNot monotonic
2025-02-20T10:16:50.728436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
144.9631 81
 
8.1%
-0.1278 77
 
7.7%
-87.6298 75
 
7.5%
135.5023 74
 
7.4%
-74.006 71
 
7.1%
139.6917 69
 
6.9%
-3.1883 68
 
6.8%
-123.1207 67
 
6.7%
-118.2437 66
 
6.6%
-73.5673 65
 
6.5%
Other values (5) 287
28.7%
ValueCountFrequency (%)
-123.1207 67
6.7%
-118.2437 66
6.6%
-87.6298 75
7.5%
-79.347 52
5.2%
-74.006 71
7.1%
-73.5673 65
6.5%
-3.1883 68
6.8%
-2.2426 63
6.3%
-0.1278 77
7.7%
135.5023 74
7.4%
ValueCountFrequency (%)
153.0251 59
5.9%
151.2093 65
6.5%
144.9631 81
8.1%
139.6917 69
6.9%
135.7681 48
4.8%
135.5023 74
7.4%
-0.1278 77
7.7%
-2.2426 63
6.3%
-3.1883 68
6.8%
-73.5673 65
6.5%

Destination Latitude
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.798711
Minimum-37.8136
Maximum55.9533
Zeros0
Zeros (%)0.0%
Negative195
Negative (%)19.5%
Memory size7.9 KiB
2025-02-20T10:16:50.754488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-37.8136
5-th percentile-37.8136
Q134.0522
median40.7128
Q349.2827
95-th percentile55.9533
Maximum55.9533
Range93.7669
Interquartile range (IQR)15.2305

Descriptive statistics

Standard deviation31.366522
Coefficient of variation (CV)1.0891641
Kurtosis0.18868278
Mean28.798711
Median Absolute Deviation (MAD)6.6606
Skewness-1.3728394
Sum28798.711
Variance983.85868
MonotonicityNot monotonic
2025-02-20T10:16:50.787328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
53.4808 81
 
8.1%
-33.8688 79
 
7.9%
55.9533 76
 
7.6%
49.2827 75
 
7.5%
34.6937 73
 
7.3%
35.0116 71
 
7.1%
45.5017 69
 
6.9%
35.6895 68
 
6.8%
51.5074 62
 
6.2%
41.8781 62
 
6.2%
Other values (5) 284
28.4%
ValueCountFrequency (%)
-37.8136 60
6.0%
-33.8688 79
7.9%
-27.4698 56
5.6%
34.0522 59
5.9%
34.6937 73
7.3%
35.0116 71
7.1%
35.6895 68
6.8%
40.7128 48
4.8%
41.8781 62
6.2%
43.651 61
6.1%
ValueCountFrequency (%)
55.9533 76
7.6%
53.4808 81
8.1%
51.5074 62
6.2%
49.2827 75
7.5%
45.5017 69
6.9%
43.651 61
6.1%
41.8781 62
6.2%
40.7128 48
4.8%
35.6895 68
6.8%
35.0116 71
7.1%

Destination Longitude
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.699003
Minimum-123.1207
Maximum153.0251
Zeros0
Zeros (%)0.0%
Negative593
Negative (%)59.3%
Memory size7.9 KiB
2025-02-20T10:16:50.830490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-123.1207
5-th percentile-123.1207
Q1-79.347
median-2.2426
Q3139.6917
95-th percentile153.0251
Maximum153.0251
Range276.1458
Interquartile range (IQR)219.0387

Descriptive statistics

Standard deviation106.34863
Coefficient of variation (CV)4.6851673
Kurtosis-1.6669785
Mean22.699003
Median Absolute Deviation (MAD)116.0011
Skewness0.052944145
Sum22699.003
Variance11310.03
MonotonicityNot monotonic
2025-02-20T10:16:50.864030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-2.2426 81
 
8.1%
151.2093 79
 
7.9%
-3.1883 76
 
7.6%
-123.1207 75
 
7.5%
135.5023 73
 
7.3%
135.7681 71
 
7.1%
-73.5673 69
 
6.9%
139.6917 68
 
6.8%
-0.1278 62
 
6.2%
-87.6298 62
 
6.2%
Other values (5) 284
28.4%
ValueCountFrequency (%)
-123.1207 75
7.5%
-118.2437 59
5.9%
-87.6298 62
6.2%
-79.347 61
6.1%
-74.006 48
4.8%
-73.5673 69
6.9%
-3.1883 76
7.6%
-2.2426 81
8.1%
-0.1278 62
6.2%
135.5023 73
7.3%
ValueCountFrequency (%)
153.0251 56
5.6%
151.2093 79
7.9%
144.9631 60
6.0%
139.6917 68
6.8%
135.7681 71
7.1%
135.5023 73
7.3%
-0.1278 62
6.2%
-2.2426 81
8.1%
-3.1883 76
7.6%
-73.5673 69
6.9%

Interactions

2025-02-20T10:16:49.259069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.053912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.294456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.518097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.801245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:49.032269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:49.285042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.085695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.335220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.622268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.832581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:49.070220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:49.336380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.134296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.368070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.660057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.885086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:49.109466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:49.372620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.175334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.401525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.683005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.920983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:49.146916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:49.402126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.214012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.448333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.733413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.957044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:49.184051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:49.447231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.251082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.486491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.770874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:48.994577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:16:49.220323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-20T10:16:50.894099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Attack TypeAttempt CountData Volume (MB)Destination LatitudeDestination LongitudeSeveritySource LatitudeSource Longitude
Attack Type1.0000.0360.0000.0000.0000.0000.0320.031
Attempt Count0.0361.000-0.035-0.0280.0610.0580.034-0.043
Data Volume (MB)0.000-0.0351.0000.0020.0410.031-0.021-0.018
Destination Latitude0.000-0.0280.0021.000-0.5420.000-0.0000.034
Destination Longitude0.0000.0610.041-0.5421.0000.0000.025-0.021
Severity0.0000.0580.0310.0000.0001.0000.0330.000
Source Latitude0.0320.034-0.021-0.0000.0250.0331.000-0.524
Source Longitude0.031-0.043-0.0180.034-0.0210.000-0.5241.000

Missing values

2025-02-20T10:16:49.501870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-20T10:16:49.536126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TimestampSource IPDestination IPAttack TypeSeverityAttempt CountData Volume (MB)Source LatitudeSource LongitudeDestination LatitudeDestination Longitude
02025-01-06 00:00:0096.32.89.114170.248.225.52DDoSCritical920835.6895139.6917-37.8136144.9631
12025-01-06 01:00:00201.61.222.13197.19.175.122RansomwareMedium1420-37.8136144.963141.8781-87.6298
22025-01-06 02:00:0051.31.102.14515.224.157.137DDoSCritical16945.5017-73.567334.6937135.5023
32025-01-06 03:00:0087.125.22.2431.233.27.146SQL InjectionMedium539134.6937135.5023-27.4698153.0251
42025-01-06 04:00:00106.16.28.14438.196.56.233RansomwareCritical1089935.0116135.768143.6510-79.3470
52025-01-06 05:00:00205.149.201.212121.82.193.137SQL InjectionHigh2605-33.8688151.209353.4808-2.2426
62025-01-06 06:00:00217.119.225.37184.230.104.167PhishingHigh47855.9533-3.188349.2827-123.1207
72025-01-06 07:00:0078.25.53.153113.96.19.214Insider ThreatCritical1139343.6510-79.347035.0116135.7681
82025-01-06 08:00:00120.142.246.239170.209.108.122Insider ThreatHigh367455.9533-3.188340.7128-74.0060
92025-01-06 09:00:0042.172.52.13567.41.30.147SQL InjectionHigh116243.6510-79.347055.9533-3.1883
TimestampSource IPDestination IPAttack TypeSeverityAttempt CountData Volume (MB)Source LatitudeSource LongitudeDestination LatitudeDestination Longitude
9902025-02-16 06:00:007.123.35.7559.3.65.95RansomwareLow295635.0116135.768145.5017-73.5673
9912025-02-16 07:00:0025.80.81.10543.248.39.37PhishingLow666735.0116135.768135.0116135.7681
9922025-02-16 08:00:00159.104.101.17131.71.249.161PhishingCritical732735.0116135.768149.2827-123.1207
9932025-02-16 09:00:006.175.158.159214.104.8.249PhishingCritical1086041.8781-87.629849.2827-123.1207
9942025-02-16 10:00:00143.95.149.21955.229.25.0MalwareMedium1541434.6937135.5023-33.8688151.2093
9952025-02-16 11:00:0090.114.86.92113.228.201.90DDoSMedium139634.0522-118.243735.0116135.7681
9962025-02-16 12:00:0075.68.119.210219.82.35.120DDoSHigh192851.5074-0.127853.4808-2.2426
9972025-02-16 13:00:00100.135.159.4659.129.70.7PhishingLow1654349.2827-123.1207-27.4698153.0251
9982025-02-16 14:00:00107.208.94.130161.148.91.75PhishingMedium6940-37.8136144.9631-33.8688151.2093
9992025-02-16 15:00:00153.17.251.196124.174.95.22MalwareMedium2547-27.4698153.025141.8781-87.6298